R Notebook
This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.
AC和PA的SMC进一步分析
分离AC和PA的SMC亚群
find markers
ds2_AC_markers <- FindAllMarkers(ds2_AC, logfc.threshold = 0.5, min.diff.pct = 0.2, only.pos = F)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 2 % ~00s
|++ | 4 % ~00s
|+++ | 5 % ~00s
|++++ | 6 % ~00s
|++++ | 8 % ~00s
|+++++ | 9 % ~00s
|+++++ | 10% ~00s
|++++++ | 11% ~00s
|+++++++ | 12% ~00s
|+++++++ | 14% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 16% ~00s
|+++++++++ | 18% ~00s
|++++++++++ | 19% ~00s
|++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++ | 24% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 26% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+ | 2 % ~00s
|++ | 4 % ~00s
|+++ | 5 % ~00s
|++++ | 7 % ~00s
|+++++ | 9 % ~00s
|++++++ | 11% ~00s
|+++++++ | 12% ~00s
|++++++++ | 14% ~00s
|+++++++++ | 16% ~00s
|+++++++++ | 18% ~00s
|++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 30% ~00s
|+++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s ds2_AC_markers_pos <- ds2_AC_markers[ds2_AC_markers$avg_logFC>0, ]
ds2_AC_markers_neg <- ds2_AC_markers[ds2_AC_markers$avg_logFC<0, ]table(Idents(ds2_AC))
0 1 2 3
1222 828 649 362 GO and KEGG
dotplot(down_enrich.go, showCategory = 10)
cnetplot(down_enrich.go, showCategory = 10)
emapplot(down_enrich.go, showCategory = 10)
GSVA
exprmat <- get_data_table(ds2_AC, highvar = T,type = "data")
clusterinfo <- ds2_AC@meta.data[,c("orig.ident","Classification1")]
mbd <- msigdbr(species = "Homo sapiens", category = "C7") # C7 免疫
msigdbr_list <- split(x = mbd$gene_symbol, f = mbd$gs_name)
immo_res <- gsva(exprmat, msigdbr_list, kcdf="Gaussian",method = "gsva", parallel.sz = 6) #gsva 在server上运行
pheatmap(immo_res, show_rownames=1, show_colnames=0,
annotation_col=clusterinfo,fontsize_row=5, wiidth=8, height=6)#绘制热图es <- data.frame(t(immo_res),stringsAsFactors=F) #添加到单细胞矩阵中，可视化相关通路的在umap上聚集情况，可理解为一个通路即一个基因
dataset1 <- AddclusterinfoData(pbmc, es)
FeaturePlot(dataset1, features = "KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS", reduction = 'umap')
multi_featureplot()
#GSEA
markers <- FindMarkers(ds2_AC, ident.1 = 3, logfc.threshold = 0.1, min.diff.pct = 0.1)
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s markers <- FindMarkers(ds2_AC, ident.1 = 3, logfc.threshold = 0.1, min.diff.pct = 0.1)
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s DEGs <- markers$avg_logFC
names(DEGs) = rownames(markers)
DEGs <- sort(DEGs,decreasing = T)
head(DEGs) SOST SUCNR1 FRZB IGFBP2 DLX6-AS1 LMO2
1.783723 1.570144 1.557937 1.504131 1.495656 1.488827 mdb_c2 <- msigdbr(species = "Homo sapiens", category = "C2")
mdb_kegg <- mdb_c2 [grep("^KEGG", mdb_c2 $gs_name),]
GO_db<-mdb_kegg %>% dplyr::select(gs_name, gene_symbol)
GSEA_res <- clusterProfiler::GSEA(DEGs, TERM2GENE = GO_db,pvalueCutoff = 0.5)preparing geneSet collections...
GSEA analysis...
Warning in fgsea(pathways = geneSets, stats = geneList, nperm = nPerm, minSize = minGSSize, :
There are ties in the preranked stats (0.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...dotplot(GSEA_res,split=".sign")+facet_grid(~.sign)
addmodulescore
geneset <- read.table("fibromyo")
dataset1 <- AddModuleScore(dataset1,features = geneset, name = 'fibromyo_score')
f("fibromyo_score1", dataset1, min.cutoff = 0)
dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')
f("V1fibromyo_score", dataset1)Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
LS0tCnRpdGxlOiAiUiBOb3RlYm9vayIKb3V0cHV0OiBodG1sX25vdGVib29rCi0tLQoKVGhpcyBpcyBhbiBbUiBNYXJrZG93bl0oaHR0cDovL3JtYXJrZG93bi5yc3R1ZGlvLmNvbSkgTm90ZWJvb2suIFdoZW4geW91IGV4ZWN1dGUgY29kZSB3aXRoaW4gdGhlIG5vdGVib29rLCB0aGUgcmVzdWx0cyBhcHBlYXIgYmVuZWF0aCB0aGUgY29kZS4gCgpUcnkgZXhlY3V0aW5nIHRoaXMgY2h1bmsgYnkgY2xpY2tpbmcgdGhlICpSdW4qIGJ1dHRvbiB3aXRoaW4gdGhlIGNodW5rIG9yIGJ5IHBsYWNpbmcgeW91ciBjdXJzb3IgaW5zaWRlIGl0IGFuZCBwcmVzc2luZyAqQ3RybCtTaGlmdCtFbnRlciouIAoKIyBBQ+WSjFBB55qEU01D6L+b5LiA5q2l5YiG5p6QCmBgYHtyfQpzb3VyY2UoInRpYW5mZW5nUndyYXBwZXJzLlIiKQpsaWJyYXJ5KG9yZy5Icy5lZy5kYikKbGlicmFyeShtc2lnZGJyKQpsaWJyYXJ5KEdTVkEpCmxpYnJhcnkoZmdzZWEpCmxpYnJhcnkoVUNlbGwpCmRzMiA8LSByZWFkUkRTKCJkczIucmRzIikKZHMwIDwtIHJlYWRSRFMoImRzMC5yZHMiKQpkczEgPC0gcmVhZFJEUygiZHMxLnJkcyIpCmBgYAoKIyMg5YiG56a7QUPlkoxQQeeahFNNQ+S6mue+pApgYGB7cn0Kc2VsZWN0LmNlbGxzIDwtIENlbGxTZWxlY3RvcihwbG90ID0gRGltUGxvdChkczIsIHJlZHVjdGlvbiA9ICJ1bWFwIikpICPljrvpmaTovrnop5LnmoTnprvnvqTnu4bog54KZHMyIDwtIHN1YnNldChkczIsIGNlbGwgPSBzZWxlY3QuY2VsbHMpCnNhdmVSRFMoZHMyLCJkczIucmRzIikKdW1hcHBsb3QoZHMyLHNwbGl0LmJ5ID0gImNvbmRpdGlvbnMiKQpkczIgPC0gZHMyICU+JSBGaW5kTmVpZ2hib3JzKGRpbXMgPSAxOjIwKSAlPiUgRmluZENsdXN0ZXJzKHJlc29sdXRpb24gPSAwLjE1KQp1bWFwcGxvdChkczIsIGdyb3VwLmJ5ID0gInNldXJhdF9jbHVzdGVycyIsc3BsaXQuYnkgPSAiY29uZGl0aW9ucyIpCklkZW50cyhkczIpIDwtIGRzMiRjb25kaXRpb25zCmRzMl9BQyA8LSBzdWJzZXQoZHMyLCBpZGVudHMgPSAiQUMiKQpkczJfUEEgPC0gc3Vic2V0KGRzMiwgaWRlbnRzID0gIlBBIikKZHMyX0FDIDwtIGRzMl9BQyAlPiUgRmluZE5laWdoYm9ycyhkaW1zID0gMToyMCkgJT4lIEZpbmRDbHVzdGVycyhyZXNvbHV0aW9uID0gMC4xKQpkczJfUEEgPC0gZHMyX1BBICU+JSBGaW5kTmVpZ2hib3JzKGRpbXMgPSAxOjIwKSAlPiUgRmluZENsdXN0ZXJzKHJlc29sdXRpb24gPSAwLjEpCgp1bWFwcGxvdChkczJfQUMpICsgc2NhbGVfeV9jb250aW51b3VzKGxpbWl0cyA9IGMoLTUsMTUpLGJyZWFrcyA9IE5VTEwpICsKICAgICAgICBzY2FsZV94X2NvbnRpbnVvdXMobGltaXRzID0gYygtNSwxNSksYnJlYWtzID0gTlVMTCkKdW1hcHBsb3QoZHMyX1BBKSsgc2NhbGVfeV9jb250aW51b3VzKGxpbWl0cyA9IGMoLTUsMTUpLGJyZWFrcyA9IE5VTEwpICsKICAgICAgICBzY2FsZV94X2NvbnRpbnVvdXMobGltaXRzID0gYygtNSwxNSksYnJlYWtzID0gTlVMTCkKYGBgCiMjIGZpbmQgbWFya2VycwpgYGB7cn0KZHMyX0FDX21hcmtlcnMgPC0gRmluZEFsbE1hcmtlcnMoZHMyX0FDLCBsb2dmYy50aHJlc2hvbGQgPSAwLjUsIG1pbi5kaWZmLnBjdCA9IDAuMiwgb25seS5wb3MgPSBGKQpkczJfQUNfbWFya2Vyc19wb3MgPC0gZHMyX0FDX21hcmtlcnNbZHMyX0FDX21hcmtlcnMkYXZnX2xvZ0ZDPjAsIF0KZHMyX0FDX21hcmtlcnNfbmVnIDwtIGRzMl9BQ19tYXJrZXJzW2RzMl9BQ19tYXJrZXJzJGF2Z19sb2dGQzwwLCBdCmBgYApgYGB7cn0KZigiRExYNiIsIGRzMCwgc3BsaXQuYnk9J2NvbmRpdGlvbnMnKQpmKCJETFg2LUFTMSIsIGRzMSkKdGFibGUoSWRlbnRzKGRzMl9BQykpCmBgYAoKCiMjIEdPIGFuZCBLRUdHCgpgYGB7cixmaWcud2lkdGg9OCxmaWcuaGVpZ2h0PTR9CiMjdXAtcmVndWxhdGVkIGdlbmVzCmdlbmVfbGlzdCA8LSBkczJfQUNfbWFya2Vyc19wb3NbZHMyX0FDX21hcmtlcnNfcG9zJGNsdXN0ZXIgPT0gMixdJGdlbmUKCnVwX2VucmljaC5nbyA8LSBlbnJpY2hHTygKICAgIGdlbmUgPSBnZW5lX2xpc3QsICMg5Z+65Zug5YiX6KGo5paH5Lu25Lit55qE5Z+65Zug5ZCN56ewCiAgICBPcmdEYiA9IG9yZy5Icy5lZy5kYiwga2V5VHlwZSA9ICJTWU1CT0wiLAogICAgb250ID0gIkFMTCIsICMg5Y+v6YCJIEJQ44CBTUbjgIFDQ++8jOS5n+WPr+S7peaMh+WumiBBTEwg5ZCM5pe26K6h566XIDMg6ICFCiAgICBwQWRqdXN0TWV0aG9kID0gImZkciIsIHB2YWx1ZUN1dG9mZiA9IDAuMDUsIHF2YWx1ZUN1dG9mZiA9IDAuMikKCmRvdHBsb3QodXBfZW5yaWNoLmdvLCBzaG93Q2F0ZWdvcnkgPSAxMCkKY25ldHBsb3QodXBfZW5yaWNoLmdvLCBzaG93Q2F0ZWdvcnkgPSAxMCkKZW1hcHBsb3QodXBfZW5yaWNoLmdvLCBzaG93Q2F0ZWdvcnkgPSAxMCkKCiMjZG93bi1yZWd1bGF0ZWQgZ2VuZXMKZ2VuZV9saXN0IDwtIGRzMl9BQ19tYXJrZXJzX25lZ1tkczJfQUNfbWFya2Vyc19uZWckY2x1c3RlciA9PSAyLF0kZ2VuZQpkb3duX2VucmljaC5nbyA8LSBlbnJpY2hHTygKICAgIGdlbmUgPSBnZW5lX2xpc3QsICMg5Z+65Zug5YiX6KGo5paH5Lu25Lit55qE5Z+65Zug5ZCN56ewCiAgICBPcmdEYiA9IG9yZy5Icy5lZy5kYiwga2V5VHlwZSA9ICJTWU1CT0wiLAogICAgb250ID0gIkFMTCIsICMg5Y+v6YCJIEJQ44CBTUbjgIFDQ++8jOS5n+WPr+S7peaMh+WumiBBTEwg5ZCM5pe26K6h566XIDMg6ICFCiAgICBwQWRqdXN0TWV0aG9kID0gImZkciIsIHB2YWx1ZUN1dG9mZiA9IDAuMDUsIHF2YWx1ZUN1dG9mZiA9IDAuMikKCmRvdHBsb3QoZG93bl9lbnJpY2guZ28sIHNob3dDYXRlZ29yeSA9IDEwKQpjbmV0cGxvdChkb3duX2VucmljaC5nbywgc2hvd0NhdGVnb3J5ID0gMTApCmVtYXBwbG90KGRvd25fZW5yaWNoLmdvLCBzaG93Q2F0ZWdvcnkgPSAxMCkKCmBgYAoKCiMjIEdTVkEKYGBge3J9CmV4cHJtYXQgPC0gZ2V0X2RhdGFfdGFibGUoZHMyX0FDLCBoaWdodmFyID0gVCx0eXBlID0gImRhdGEiKQpjbHVzdGVyaW5mbyA8LSBkczJfQUNAbWV0YS5kYXRhWyxjKCJvcmlnLmlkZW50IiwiQ2xhc3NpZmljYXRpb24xIildCm1iZCA8LSBtc2lnZGJyKHNwZWNpZXMgPSAiSG9tbyBzYXBpZW5zIiwgY2F0ZWdvcnkgPSAiQzciKSAjIEM3IOWFjeeWqwptc2lnZGJyX2xpc3QgPC0gc3BsaXQoeCA9IG1iZCRnZW5lX3N5bWJvbCwgZiA9IG1iZCRnc19uYW1lKQoKaW1tb19yZXMgPC0gZ3N2YShleHBybWF0LCBtc2lnZGJyX2xpc3QsIGtjZGY9IkdhdXNzaWFuIixtZXRob2QgPSAiZ3N2YSIsIHBhcmFsbGVsLnN6ID0gNikgI2dzdmEg5Zyoc2VydmVy5LiK6L+Q6KGMCnBoZWF0bWFwKGltbW9fcmVzLCBzaG93X3Jvd25hbWVzPTEsIHNob3dfY29sbmFtZXM9MCwgCiAgICAgICAgIGFubm90YXRpb25fY29sPWNsdXN0ZXJpbmZvLGZvbnRzaXplX3Jvdz01LCB3aWlkdGg9OCwgaGVpZ2h0PTYpI+e7mOWItueDreWbvgpgYGAKCgpgYGB7cn0KZXMgPC0gZGF0YS5mcmFtZSh0KGltbW9fcmVzKSxzdHJpbmdzQXNGYWN0b3JzPUYpICAj5re75Yqg5Yiw5Y2V57uG6IOe55+p6Zi15Lit77yM5Y+v6KeG5YyW55u45YWz6YCa6Lev55qE5ZyodW1hcOS4iuiBmumbhuaDheWGte+8jOWPr+eQhuino+S4uuS4gOS4qumAmui3r+WNs+S4gOS4quWfuuWboApkYXRhc2V0MSA8LSBBZGRjbHVzdGVyaW5mb0RhdGEocGJtYywgZXMpCkZlYXR1cmVQbG90KGRhdGFzZXQxLCBmZWF0dXJlcyA9ICJLRUdHX1BSSU1BUllfQklMRV9BQ0lEX0JJT1NZTlRIRVNJUyIsIHJlZHVjdGlvbiA9ICd1bWFwJykKbXVsdGlfZmVhdHVyZXBsb3QoKQoKYGBgCgojR1NFQQpgYGB7cn0KbGlicmFyeShjbHVzdGVyUHJvZmlsZXIpCm1hcmtlcnMgPC0gRmluZE1hcmtlcnMoZHMyX0FDLCBpZGVudC4xID0gMywgbG9nZmMudGhyZXNob2xkID0gMC4xLCBtaW4uZGlmZi5wY3QgPSAwLjEpCkRFR3MgPC0gbWFya2VycyRhdmdfbG9nRkMKbmFtZXMoREVHcykgPSByb3duYW1lcyhtYXJrZXJzKQpERUdzIDwtIHNvcnQoREVHcyxkZWNyZWFzaW5nID0gVCkKaGVhZChERUdzKQoKIyBHT19kYiA8LSBtc2lnZGJyKHNwZWNpZXMgPSAiSG9tbyBzYXBpZW5zIixjYXRlZ29yeSA9ICJDMiIpICU+JSAKIyAgIGRwbHlyOjpzZWxlY3QoZ3NfZXhhY3Rfc291cmNlLGdlbmVfc3ltYm9sKSAjQzUgR08gIEM3IOWFjeeWqwoKbWRiX2MyIDwtIG1zaWdkYnIoc3BlY2llcyA9ICJIb21vIHNhcGllbnMiLCBjYXRlZ29yeSA9ICJDMiIpCm1kYl9rZWdnIDwtICBtZGJfYzIgW2dyZXAoIl5LRUdHIiwgbWRiX2MyICRnc19uYW1lKSxdCkdPX2RiPC1tZGJfa2VnZyAlPiUgZHBseXI6OnNlbGVjdChnc19uYW1lLCBnZW5lX3N5bWJvbCkKCkdTRUFfcmVzIDwtIGNsdXN0ZXJQcm9maWxlcjo6R1NFQShERUdzLCBURVJNMkdFTkUgPSBHT19kYixwdmFsdWVDdXRvZmYgPSAwLjUpCgpkb3RwbG90KEdTRUFfcmVzLHNwbGl0PSIuc2lnbiIpK2ZhY2V0X2dyaWQofi5zaWduKQoKZW5yaWNocGxvdDo6Z3NlYXBsb3QyKEdTRUFfcmVzLCBnZW5lU2V0SUQgPSAxLCB0aXRsZSA9IEdTRUFfcmVzJERlc2NyaXB0aW9uWzFdKQoKZm9yKGkgaW4gc2VxX2Fsb25nKEdTRUFfcmVzQHJlc3VsdCRJRCkpewogIGdzZWFwbG90MihHU0VBX3JlcywgZ2VuZVNldElEID0gaSwgdGl0bGUgPSBHU0VBX3Jlc0ByZXN1bHQkSURbaV0pCn0KCmBgYAoKCiMgIGFkZG1vZHVsZXNjb3JlCmBgYHtyfQpnZW5lc2V0IDwtIHJlYWQudGFibGUoImZpYnJvbXlvIikKZGF0YXNldDEgPC0gQWRkTW9kdWxlU2NvcmUoZGF0YXNldDEsZmVhdHVyZXMgPSBnZW5lc2V0LCBuYW1lID0gJ2ZpYnJvbXlvX3Njb3JlJykKZigiZmlicm9teW9fc2NvcmUxIiwgZGF0YXNldDEsIG1pbi5jdXRvZmYgPSAwKQpkYXRhc2V0MSA8LSBBZGRNb2R1bGVTY29yZV9VQ2VsbChkYXRhc2V0MSxmZWF0dXJlcyA9IGdlbmVzZXQsIG5hbWUgPSAnZmlicm9teW9fc2NvcmUnKQpmKCJWMWZpYnJvbXlvX3Njb3JlIiwgZGF0YXNldDEpCmBgYAoKQWRkIGEgbmV3IGNodW5rIGJ5IGNsaWNraW5nIHRoZSAqSW5zZXJ0IENodW5rKiBidXR0b24gb24gdGhlIHRvb2xiYXIgb3IgYnkgcHJlc3NpbmcgKkN0cmwrQWx0K0kqLgoKV2hlbiB5b3Ugc2F2ZSB0aGUgbm90ZWJvb2ssIGFuIEhUTUwgZmlsZSBjb250YWluaW5nIHRoZSBjb2RlIGFuZCBvdXRwdXQgd2lsbCBiZSBzYXZlZCBhbG9uZ3NpZGUgaXQgKGNsaWNrIHRoZSAqUHJldmlldyogYnV0dG9uIG9yIHByZXNzICpDdHJsK1NoaWZ0K0sqIHRvIHByZXZpZXcgdGhlIEhUTUwgZmlsZSkuCgpUaGUgcHJldmlldyBzaG93cyB5b3UgYSByZW5kZXJlZCBIVE1MIGNvcHkgb2YgdGhlIGNvbnRlbnRzIG9mIHRoZSBlZGl0b3IuIENvbnNlcXVlbnRseSwgdW5saWtlICpLbml0KiwgKlByZXZpZXcqIGRvZXMgbm90IHJ1biBhbnkgUiBjb2RlIGNodW5rcy4gSW5zdGVhZCwgdGhlIG91dHB1dCBvZiB0aGUgY2h1bmsgd2hlbiBpdCB3YXMgbGFzdCBydW4gaW4gdGhlIGVkaXRvciBpcyBkaXNwbGF5ZWQuCg==